From 68213b835b9f15cb449ad2037a8b51c17a3de07b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 14 Mar 2016 22:10:14 +0000
Subject: [PATCH] Makefile
---
src/network_kernels.cu | 281 ++++++++++++++++++++++++--------------------------------
1 files changed, 121 insertions(+), 160 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 4fc361d..730634e 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -1,61 +1,87 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
extern "C" {
#include <stdio.h>
#include <time.h>
+#include <assert.h>
#include "network.h"
#include "image.h"
#include "data.h"
#include "utils.h"
-#include "params.h"
+#include "parser.h"
#include "crop_layer.h"
#include "connected_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
#include "detection_layer.h"
#include "convolutional_layer.h"
+#include "activation_layer.h"
#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
-#include "cost_layer.h"
+#include "avgpool_layer.h"
#include "normalization_layer.h"
+#include "cost_layer.h"
+#include "local_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
+#include "route_layer.h"
+#include "shortcut_layer.h"
+#include "blas.h"
}
-extern "C" float * get_network_output_gpu_layer(network net, int i);
-extern "C" float * get_network_delta_gpu_layer(network net, int i);
-float *get_network_output_gpu(network net);
+float * get_network_output_gpu_layer(network net, int i);
+float * get_network_delta_gpu_layer(network net, int i);
+float * get_network_output_gpu(network net);
void forward_network_gpu(network net, network_state state)
{
int i;
for(i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- forward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
+ state.index = i;
+ layer l = net.layers[i];
+ if(l.delta_gpu){
+ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
}
- else if(net.types[i] == DECONVOLUTIONAL){
- forward_deconvolutional_layer_gpu(*(deconvolutional_layer *)net.layers[i], state);
+ if(l.type == CONVOLUTIONAL){
+ forward_convolutional_layer_gpu(l, state);
+ } else if(l.type == DECONVOLUTIONAL){
+ forward_deconvolutional_layer_gpu(l, state);
+ } else if(l.type == ACTIVE){
+ forward_activation_layer_gpu(l, state);
+ } else if(l.type == LOCAL){
+ forward_local_layer_gpu(l, state);
+ } else if(l.type == DETECTION){
+ forward_detection_layer_gpu(l, state);
+ } else if(l.type == CONNECTED){
+ forward_connected_layer_gpu(l, state);
+ } else if(l.type == RNN){
+ forward_rnn_layer_gpu(l, state);
+ } else if(l.type == CRNN){
+ forward_crnn_layer_gpu(l, state);
+ } else if(l.type == CROP){
+ forward_crop_layer_gpu(l, state);
+ } else if(l.type == COST){
+ forward_cost_layer_gpu(l, state);
+ } else if(l.type == SOFTMAX){
+ forward_softmax_layer_gpu(l, state);
+ } else if(l.type == NORMALIZATION){
+ forward_normalization_layer_gpu(l, state);
+ } else if(l.type == MAXPOOL){
+ forward_maxpool_layer_gpu(l, state);
+ } else if(l.type == AVGPOOL){
+ forward_avgpool_layer_gpu(l, state);
+ } else if(l.type == DROPOUT){
+ forward_dropout_layer_gpu(l, state);
+ } else if(l.type == ROUTE){
+ forward_route_layer_gpu(l, net);
+ } else if(l.type == SHORTCUT){
+ forward_shortcut_layer_gpu(l, state);
}
- else if(net.types[i] == COST){
- forward_cost_layer_gpu(*(cost_layer *)net.layers[i], state);
- }
- else if(net.types[i] == CONNECTED){
- forward_connected_layer_gpu(*(connected_layer *)net.layers[i], state);
- }
- else if(net.types[i] == DETECTION){
- forward_detection_layer_gpu(*(detection_layer *)net.layers[i], state);
- }
- else if(net.types[i] == MAXPOOL){
- forward_maxpool_layer_gpu(*(maxpool_layer *)net.layers[i], state);
- }
- else if(net.types[i] == SOFTMAX){
- forward_softmax_layer_gpu(*(softmax_layer *)net.layers[i], state);
- }
- else if(net.types[i] == DROPOUT){
- forward_dropout_layer_gpu(*(dropout_layer *)net.layers[i], state);
- }
- else if(net.types[i] == CROP){
- forward_crop_layer_gpu(*(crop_layer *)net.layers[i], state);
- }
- state.input = get_network_output_gpu_layer(net, i);
+ state.input = l.output_gpu;
}
}
@@ -63,37 +89,50 @@
{
int i;
float * original_input = state.input;
+ float * original_delta = state.delta;
for(i = net.n-1; i >= 0; --i){
+ state.index = i;
+ layer l = net.layers[i];
if(i == 0){
state.input = original_input;
- state.delta = 0;
+ state.delta = original_delta;
}else{
- state.input = get_network_output_gpu_layer(net, i-1);
- state.delta = get_network_delta_gpu_layer(net, i-1);
+ layer prev = net.layers[i-1];
+ state.input = prev.output_gpu;
+ state.delta = prev.delta_gpu;
}
- if(net.types[i] == CONVOLUTIONAL){
- backward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- backward_deconvolutional_layer_gpu(*(deconvolutional_layer *)net.layers[i], state);
- }
- else if(net.types[i] == COST){
- backward_cost_layer_gpu(*(cost_layer *)net.layers[i], state);
- }
- else if(net.types[i] == CONNECTED){
- backward_connected_layer_gpu(*(connected_layer *)net.layers[i], state);
- }
- else if(net.types[i] == DETECTION){
- backward_detection_layer_gpu(*(detection_layer *)net.layers[i], state);
- }
- else if(net.types[i] == MAXPOOL){
- backward_maxpool_layer_gpu(*(maxpool_layer *)net.layers[i], state);
- }
- else if(net.types[i] == DROPOUT){
- backward_dropout_layer_gpu(*(dropout_layer *)net.layers[i], state);
- }
- else if(net.types[i] == SOFTMAX){
- backward_softmax_layer_gpu(*(softmax_layer *)net.layers[i], state);
+ if(l.type == CONVOLUTIONAL){
+ backward_convolutional_layer_gpu(l, state);
+ } else if(l.type == DECONVOLUTIONAL){
+ backward_deconvolutional_layer_gpu(l, state);
+ } else if(l.type == ACTIVE){
+ backward_activation_layer_gpu(l, state);
+ } else if(l.type == LOCAL){
+ backward_local_layer_gpu(l, state);
+ } else if(l.type == MAXPOOL){
+ if(i != 0) backward_maxpool_layer_gpu(l, state);
+ } else if(l.type == AVGPOOL){
+ if(i != 0) backward_avgpool_layer_gpu(l, state);
+ } else if(l.type == DROPOUT){
+ backward_dropout_layer_gpu(l, state);
+ } else if(l.type == DETECTION){
+ backward_detection_layer_gpu(l, state);
+ } else if(l.type == NORMALIZATION){
+ backward_normalization_layer_gpu(l, state);
+ } else if(l.type == SOFTMAX){
+ if(i != 0) backward_softmax_layer_gpu(l, state);
+ } else if(l.type == CONNECTED){
+ backward_connected_layer_gpu(l, state);
+ } else if(l.type == RNN){
+ backward_rnn_layer_gpu(l, state);
+ } else if(l.type == CRNN){
+ backward_crnn_layer_gpu(l, state);
+ } else if(l.type == COST){
+ backward_cost_layer_gpu(l, state);
+ } else if(l.type == ROUTE){
+ backward_route_layer_gpu(l, net);
+ } else if(l.type == SHORTCUT){
+ backward_shortcut_layer_gpu(l, state);
}
}
}
@@ -102,88 +141,33 @@
{
int i;
int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer_gpu(layer, update_batch, net.learning_rate, net.momentum, net.decay);
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- update_deconvolutional_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer_gpu(layer, update_batch, net.learning_rate, net.momentum, net.decay);
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ update_convolutional_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == DECONVOLUTIONAL){
+ update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
+ } else if(l.type == CONNECTED){
+ update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == RNN){
+ update_rnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == CRNN){
+ update_crnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == LOCAL){
+ update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
}
}
}
-float * get_network_output_gpu_layer(network net, int i)
-{
- if(net.types[i] == CONVOLUTIONAL){
- return ((convolutional_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- return ((deconvolutional_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == DETECTION){
- return ((detection_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == CONNECTED){
- return ((connected_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == MAXPOOL){
- return ((maxpool_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == CROP){
- return ((crop_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == SOFTMAX){
- return ((softmax_layer *)net.layers[i]) -> output_gpu;
- }
- else if(net.types[i] == DROPOUT){
- return get_network_output_gpu_layer(net, i-1);
- }
- return 0;
-}
-
-float * get_network_delta_gpu_layer(network net, int i)
-{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.delta_gpu;
- } else if(net.types[i] == DROPOUT){
- if(i == 0) return 0;
- return get_network_delta_gpu_layer(net, i-1);
- }
- return 0;
-}
-
float train_network_datum_gpu(network net, float *x, float *y)
{
network_state state;
+ state.index = 0;
+ state.net = net;
int x_size = get_network_input_size(net)*net.batch;
int y_size = get_network_output_size(net)*net.batch;
+ if(net.layers[net.n-1].type == DETECTION) y_size = net.layers[net.n-1].truths*net.batch;
if(!*net.input_gpu){
*net.input_gpu = cuda_make_array(x, x_size);
*net.truth_gpu = cuda_make_array(y, y_size);
@@ -192,53 +176,28 @@
cuda_push_array(*net.truth_gpu, y, y_size);
}
state.input = *net.input_gpu;
+ state.delta = 0;
state.truth = *net.truth_gpu;
state.train = 1;
forward_network_gpu(net, state);
backward_network_gpu(net, state);
- if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
float error = get_network_cost(net);
+ if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
return error;
}
float *get_network_output_layer_gpu(network net, int i)
{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- cuda_pull_array(layer.output_gpu, layer.output, layer.outputs*layer.batch);
- return layer.output;
- }
- else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- int outputs = get_detection_layer_output_size(layer);
- cuda_pull_array(layer.output_gpu, layer.output, outputs*layer.batch);
- return layer.output;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- pull_softmax_layer_output(layer);
- return layer.output;
- }
- return 0;
+ layer l = net.layers[i];
+ cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
+ return l.output;
}
float *get_network_output_gpu(network net)
{
int i;
- for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
+ for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return get_network_output_layer_gpu(net, i);
}
@@ -246,6 +205,8 @@
{
int size = get_network_input_size(net) * net.batch;
network_state state;
+ state.index = 0;
+ state.net = net;
state.input = cuda_make_array(input, size);
state.truth = 0;
state.train = 0;
--
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